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What I learned, and keep learning.

Thorough, first-principles explainers of the data science I studied at the University of Melbourne — and the topics I taught. Writing each one from scratch is how I keep the fundamentals sharp. Built one topic at a time; 1 live so far.

Natural Language ProcessingTokens, TF-IDF, embeddings, transformers
Live
Statistical Machine LearningBias-variance, regularisation, generalisation
Next
Bayesian StatisticsPriors, posteriors, MCMC
Planned
Cluster & Cloud ComputingMPI, Spark, HPC at scale
Planned
Linear AlgebraVectors, eigenvalues, the SVD
Planned
Linear Statistical ModelsOLS, inference, diagnostics
Planned
Database SystemsRelational model, SQL, indexing
Planned
Artificial IntelligenceSearch, logic, planning
Planned